Improved task-based functional MRI language mapping in patients with brain tumors through marchenko-pastur principal component analysis denoising

B Ades-Aron, G Lemberskiy, J Veraart, J Golfinos… - Radiology, 2021 - pubs.rsna.org
Radiology, 2021pubs.rsna.org
Background Functional MRI improves preoperative planning in patients with brain tumors,
but task-correlated signal intensity changes are only 2%–3% above baseline. This makes
accurate functional mapping challenging. Marchenko-Pastur principal component analysis
(MP-PCA) provides a novel strategy to separate functional MRI signal from noise without
requiring user input or prior data representation. Purpose To determine whether MP-PCA
denoising improves activation magnitude for task-based functional MRI language mapping …
Background
Functional MRI improves preoperative planning in patients with brain tumors, but task-correlated signal intensity changes are only 2%–3% above baseline. This makes accurate functional mapping challenging. Marchenko-Pastur principal component analysis (MP-PCA) provides a novel strategy to separate functional MRI signal from noise without requiring user input or prior data representation.
Purpose
To determine whether MP-PCA denoising improves activation magnitude for task-based functional MRI language mapping in patients with brain tumors.
Materials and Methods
In this Health Insurance Portability and Accountability Act–compliant study, MP-PCA performance was first evaluated by using simulated functional MRI data with a known ground truth. Right-handed, left-language–dominant patients with brain tumors who successfully performed verb generation, sentence completion, and finger tapping functional MRI tasks were retrospectively identified between January 2017 and August 2018. On the group level, for each task, histograms of z scores for original and MP-PCA denoised data were extracted from relevant regions and contralateral homologs were seeded by a neuroradiologist blinded to functional MRI findings. Z scores were compared with paired two-sided t tests, and distributions were compared with effect size measurements and the Kolmogorov-Smirnov test. The number of voxels with a z score greater than 3 was used to measure task sensitivity relative to task duration.
Results
Twenty-three patients (mean age ± standard deviation, 43 years ± 18; 13 women) were evaluated. MP-PCA denoising led to a higher median z score of task-based functional MRI voxel activation in left hemisphere cortical regions for verb generation (from 3.8 ± 1.0 to 4.5 ± 1.4; P < .001), sentence completion (from 3.7 ± 1.0 to 4.3 ± 1.4; P < .001), and finger tapping (from 6.9 ± 2.4 to 7.9 ± 2.9; P < .001). Median z scores did not improve in contralateral homolog regions for verb generation (from −2.7 ± 0.54 to −2.5 ± 0.40; P = .90), sentence completion (from −2.3 ± 0.21 to −2.4 ± 0.37; P = .39), or finger tapping (from −2.3 ± 1.20 to −2.7 ± 1.40; P = .07). Individual functional MRI task durations could be truncated by at least 40% after MP-PCA without degradation of clinically relevant correlations between functional cortex and functional MRI tasks.
Conclusion
Denoising with Marchenko-Pastur principal component analysis led to higher task correlations in relevant cortical regions during functional MRI language mapping in patients with brain tumors.
© RSNA, 2020
See also the editorial by Field and Birn.
Radiological Society of North America
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